## [1] "/Users/jouta1/Desktop/github.nosync/tlao-public"
pre-exclusion N and age
## # A tibble: 1 × 1
## mean_age
## <dbl>
## 1 6.55
subjects excluded (20): - technical difficulties (5): s028, s029, s030, s042, s078 (i.e. stimuli didn’t loop, pyhab keeps freezing, no sound, ) - experimenter error (2): s035, s064 (forgetting to record shared screen, not recording session) - inattention (4): s039, s053, s058, s073 - poor video quality (1): s043 - external distraction (3): s068, s069, s075 (siblings, caregiver) - trial exclusions resulting in no trials left/accumulated looking time less than 3 seconds (5): s038, s049, s062, s067, s071 (and thus had to exclude non-consecutive test trials)
## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## # A tibble: 1 × 1
## n
## <int>
## 1 36
## [1] 5.07 8.89
## [1] 6.84
## # A tibble: 2 × 3
## trialType mean sd
## <chr> <dbl> <dbl>
## 1 expected 22.0 18.2
## 2 unexpected 20.2 16.2
# trianing effect linear model
##
## Call:
## lm(formula = voe_effect ~ train_duration, data = d_voe)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.27 -15.48 1.21 5.70 42.54
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.70 4.64 0.58 0.56
## train_durationtrain shorter than median -8.16 6.22 -1.31 0.20
##
## Residual standard error: 18.5 on 34 degrees of freedom
## Multiple R-squared: 0.0482, Adjusted R-squared: 0.0202
## F-statistic: 1.72 on 1 and 34 DF, p-value: 0.198
d_icatcher_stats <- d_icatcher_plot_first_pair %>%
filter(Trials.trialType == "test") %>%
mutate(order = ifelse(condLabel %in% c(1, 4), "expected_first", "unexpected_first")) %>%
filter(!is.nan(Log_looks) & !is.infinite(Log_looks)) %>%
mutate(Index = row_number())
model <- lmer(Looks.duration_onLooks ~ trialType + (1 | SubjectInfo.subjID), data = d_icatcher_stats)
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Looks.duration_onLooks ~ trialType + (1 | SubjectInfo.subjID)
## Data: d_icatcher_stats
##
## REML criterion at convergence: 590
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.471 -0.523 -0.352 0.494 2.029
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjectInfo.subjID (Intercept) 120 11.0
## Residual 179 13.4
## Number of obs: 71, groups: SubjectInfo.subjID, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 22.03 2.88 59.74 7.64 2.1e-10 ***
## trialTypeunexpected -1.63 3.19 34.82 -0.51 0.61
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trlTypnxpct -0.542
#with log-transformation
model_log <- lmer(Log_looks ~ trialType + (1 | SubjectInfo.subjID) + age_months_decimal + order, data = d_icatcher_stats)
summary(model_log)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Log_looks ~ trialType + (1 | SubjectInfo.subjID) + age_months_decimal +
## order
## Data: d_icatcher_stats
##
## REML criterion at convergence: 178
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7609 -0.6118 0.0184 0.5948 1.7881
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjectInfo.subjID (Intercept) 0.370 0.608
## Residual 0.408 0.639
## Number of obs: 71, groups: SubjectInfo.subjID, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.6468 0.8078 33.8196 3.28 0.0024 **
## trialTypeunexpected -0.0339 0.1521 34.7899 -0.22 0.8249
## age_months_decimal 0.0275 0.1107 33.3795 0.25 0.8051
## orderunexpected_first -0.2481 0.2632 33.1789 -0.94 0.3526
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trlTyp ag_mn_
## trlTypnxpct -0.081
## ag_mnths_dc -0.973 -0.014
## ordrnxpctd_ -0.373 0.010 0.243